WorldCat Identities

Osborne, Jason W.

Overview
Works: 20 works in 69 publications in 1 language and 1,418 library holdings
Roles: Author, Editor
Classifications: H62, 001.42
Publication Timeline
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Most widely held works by Jason W Osborne
Best practices in quantitative methods by Jason W Osborne( Book )

17 editions published between 2007 and 2008 in English and held by 352 WorldCat member libraries worldwide

This edited work is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource to go to for practical and sound advice from leading experts in quantitative methods
Best practices in data cleaning : a complete guide to everything you need to do before and after collecting your data by Jason W Osborne( Book )

13 editions published between 2012 and 2013 in English and held by 175 WorldCat member libraries worldwide

"Many researchers jump straight from data collection to data analysis without realizing how analyses and hypothesis tests can go profoundly wrong without clean data. This book provides a clear, step-by-step process to examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are research-based and will motivate change in practice by empirically demonstrating for each topic the benefits of following best practices and the potential consequences of not following these guidelines. If your goal is to do the best research you can do, draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, and report results that are most likely to be replicated by other researchers, then this basic guidebook is indispensible."--Publisher's website
Best practices in logistic regression by Jason W Osborne( Book )

12 editions published between 2014 and 2015 in English and held by 131 WorldCat member libraries worldwide

Jason W. Osborne's Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers' basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne's applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension
The advantages of hierarchical linear modeling by Jason W Osborne( Book )

2 editions published in 2000 in English and held by 109 WorldCat member libraries worldwide

Regression & linear modeling : best practices and modern methods by Jason W Osborne( Book )

5 editions published between 2016 and 2017 in English and held by 61 WorldCat member libraries worldwide

Exploratory factor analysis with SAS by Jason W Osborne( Book )

4 editions published in 2016 in English and held by 7 WorldCat member libraries worldwide

Best practices in exloratory factor analysis by Jason W Osborne( Book )

2 editions published in 2014 in English and held by 6 WorldCat member libraries worldwide

"Best practices in exploraatory factor analysis (EFA) is a practitioner-enable look at this popular and often misundestood statistical technique. We avoid formulas and matrix algebra, instead focusing on evidence-based best practices so you can focus on getting the most from your data."--Page 4 of cover
Measuring metacognition : validation of the assessment of cognition monitoring effectiveness by Jason W Osborne( )

1 edition published in 1998 in English and held by 3 WorldCat member libraries worldwide

Normalizing Data Transformations. Eric Digest by Jason W Osborne( Book )

1 edition published in 2002 in English and held by 1 WorldCat member library worldwide

The goal of this Digest is to explore some of the issues involved in data transformation, with particular focus in the use of data transformation for the normalization of variables. The Digest is intended to serve as an aid to researchers who do not have extensive mathematical backgrounds or who had not had extensive exposure to this issue. It focuses on three of the most common data transformations used to improve normality: (1) square root; (2) logarithmic; and (3) inverse transformations. It is recommended that researchers always examine and understand data prior to performing analyses, and that they then know the requirements of the data analysis technique to be used. Data transformation should be used with care, and never unless there is a clear reason, and the researcher must ensure that the variable is anchored at a place where the transformation will have the optimal effect. (Sld)
The Effects of Minimum Values on Data Transformations by Jason W Osborne( Book )

1 edition published in 2002 in English and held by 1 WorldCat member library worldwide

Data transformations are commonly used tools in quantitative analysis of data. However, data transformations can be a mixed blessing to researchers, improving the quality of the analysis while at the same time making the interpretation of the results difficult. Few, if any, statistical texts discuss the tremendous influence a distribution's minimum value has on the outcome of a transformation. The goal of this paper is to promote thoughtful and informed use of data transformation. The focus is on three common data transformations: square root, logarithmic, and inverse transformations. All three are curvilinear transformations that change the nature of the variable to a certain extent. Examples illustrate the importance of the minimum value of a distribution should the researcher intend to use data transformation on that variable. (Contains 10 references.) (Sld)
Educational Psychology from a Statistician's Perspective: A Review of the Quantitative Quality of Our Field by Jason W Osborne( Book )

1 edition published in 2001 in English and held by 1 WorldCat member library worldwide

The goal of this study was to assess the statistical health of educational psychology literature, both current and past, to: (1) determine the range of effect sizes observed in the current literature (1998-1999); (2) determine the range of observed (or a posteriori) power in the current literature; (3) compare these two statistics to that of the discipline in past years (1939 and 1969); (4) assess the proportion of articles from each of those years reporting testing assumptions of statistical tests, effect size, or power; and (5) assess the reliability of measures used in this research. In all, 55 from 1969 and 96 from the current period were included in these analyses. Results were encouraging, suggesting that most educational psychology research encounters at least moderate (d=0.50) effect sizes, with average power (0.73) that is increasing over that observed in 1969. However, with only 36% of educational psychology studies showing acceptable levels of power (0.80 according to Cohen), only 17% reporting effect sizes, only 8% reporting testing assumptions of statistical tests, and only 2% reporting power, there is still a great deal of room for improvement in the field. (Author/SLD)
Multiple Regression Assumptions. Eric Digest by Jason W Osborne( Book )

1 edition published in 2002 in English and held by 1 WorldCat member library worldwide

This Digest presents a discussion of the assumptions of multiple regression that is tailored to the practicing researcher. The focus is on the assumptions of multiple regression that are not robust to violation, and that researchers can deal with if violated. Assumptions of normality, linearity, reliability of measurement, and homoscedasticity are considered. Checking these assumptions carries significant benefits for the researcher, and making sure an analysis meets the associated assumptions helps avoid Type I and ii errors. Attending to such issues as attenuation due to low reliability, curvilinearity, and nonnormality often boosts effect sizes, usually a desirable outcome. There are many nonparametric statistical techniques available to researchers when the assumptions of a parametric statistical technique are not met. These are often somewhat lower in power than parametric techniques, but they provide valuable alternatives for researchers. (Sld)
Testing Stereotype Threat: Does Anxiety Explain Race and Sex Differencesin Achievement? by Jason W Osborne( Book )

1 edition published in 2000 in English and held by 1 WorldCat member library worldwide

The stereotype threat theory of C. Steele (1992, 1997) attempts to explain the underperformance of minority students in academic domains and of women in mathematics. Steele asserts that situational self-relevance of negative group stereotypes in testing situations increases the anxiety these students experience, and that these differential anxiety levels explain performance differences. Research shows that manipulation of stereotype threat can affect academic performance. However, there has been little research testing whether anxiety does at least partially explain the relationship between race and achievement. The goal of this study was to examine whether anxiety will explain racial differences in academic performance and gender differences in mathematics performance in the context of a nationally representative sample of high school seniors. Data were drawn from the senior cohort data file of the High School and Beyond study, a cohort that initially consisted of 28,240 seniors from 1,015 schools. Partial mediation was observed, with anxiety explaining significant portions of the racial differences in academic performance. Anxiety also partially explained sex differences in mathematics achievement, although the effect sizes were very small. These results provide general support for Steele's stereotype threat hypothesis. (Contains 3 tables and 54 references.) (Author/SLD)
Identification with Academics, Academic Outcomes, and Withdrawal from School in High School Students: Is There a Racial Paradox? by Jason W Osborne( Book )

1 edition published in 2002 in English and held by 1 WorldCat member library worldwide

Identification with academics, or the extent to which academic is central to self-concept, has been linked to academic outcomes conceptually and empirically, at least in samples of white college students. However, Claude Steele's Stereotype Threat Hypothesis (1997) proposes something of a racial paradox, by which the most identified students of color might be most at risk for poor academic outcomes. The goal of this study was to test this racial paradox as it relates to identification with academics. High school students were followed for 2 years, with a total of 131 students from whom there were complete data. Simple main effects of identification with academics were observed, with increasing identification associated with higher grades, lower absenteeism, and fewer behavioral referrals. However, the racial paradox was evident in dropout rates. White students became less likely to withdraw as identification increased, but students of color became more likely to withdraw. Taken in the context of previous research, this work holds significant implications for dropout prevention. (Contains 32 references.) (Author/SLD)
Exploratory Factor Analysis with SAS by Jason W Osborne( Book )

1 edition published in 2016 in English and held by 1 WorldCat member library worldwide

Identification with Academics and Academic Outcomes in Secondary Students by Jason W Osborne( Book )

1 edition published in 2001 in English and held by 1 WorldCat member library worldwide

Identification with academics is the extent to which an individual bases his or her self-esteem on outcomes in the academic domain. Theory suggests that students who are highly identified with academics should be more motivated to succeed in a domain, and thus more likely to experience desirable academic outcomes and avoid undesirable outcomes, such as dropping out of school or obtaining poor grades. Two studies examined the validity of this hypothesis. In the first study, data from the National Education Longitudinal Study of 1988 show that 2 years prior to dropping out of school, substantial differences in identification are present between those who drop out and those who do not. Study 2, which involved the entire entering ninth-grade class of a high school, showed that identification with academics is related to psychological variables such as holding learning goals, intrinsic valuing of academics, self-regulation, holding a mastery orientation, and amount of processing course material receives. Identification with academics also prospectively predicts future academic outcomes such as grades, behavioral referrals, and absenteeism. (Contains 17 references.) (Author/SLD)
The Advantages of Hierarchical Linear Modeling. Eric/e Digest by Jason W Osborne( Book )

1 edition published in 2000 in English and held by 1 WorldCat member library worldwide

This digest introduces hierarchical data structure, describes how hierarchical models work, and presents three approaches to analyzing hierarchical data. Hierarchical, or nested data, present several problems for analysis. People or creatures that exist within hierarchies tend to be more similar to each other than people randomly sampled from the entire population; for this reason, observations based on these individuals are not fully independent. Hierarchical linear modeling can address problems caused by this situation. The basic concept is similar to that of ordinary least squares regression. On a base level (usually the individual), an outcome variable is predicted as a function of a linear combination of one or more level 1 variables. On subsequent levels, the level 1 slope (or slopes) and intercept become dependent variables being predicted from level 2 variables. Through this process, the effects of level 1 variables on the outcome are accurately modeled, and the effects of level 2 variables are also modeled on the outcome. Cross-level interactions can be modeled. Data from the National Education Longitudinal Survey of 1988 are used to illustrate disaggregated, aggregated, and hierarchical analyses. These analyses reveal the need for multilevel analysis of multilevel data. (Sld)
Race and academic disidentification by Jason W Osborne( )

1 edition published in 1997 in English and held by 1 WorldCat member library worldwide

Normalizing data transformations by Jason W Osborne( )

1 edition published in 2002 in English and held by 0 WorldCat member libraries worldwide

Multiple regression assumptions by Jason W Osborne( )

1 edition published in 2002 in English and held by 0 WorldCat member libraries worldwide

 
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Best practices in quantitative methods
Alternative Names
Osborne, J. W.

Languages
English (68)

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